Detecting performance interference in cloud-based web services

Web services have increasingly begun to rely on public cloud platforms. The virtualization technologies employed by public clouds can however trigger contention between virtual machines (VMs) for shared physical machine (PM) resources thereby leading to performance problems for the Web service. Past studies have exploited PM level performance metrics such as Clock Cycles Per Instruction to detect such platform induced performance interference. Unfortunately, public cloud customers do not have access to such metrics. They can typically only access VM-level metrics and application level metrics such as transaction response times and such metrics alone are often not useful for detecting inter-VM contention. This poses a difficult challenge to Web service operators for detecting and managing platform induced performance interference issues inside the cloud. We propose a machine learning based interference detection technique to address this problem. The technique applies collaborative filtering to predict whether a given transaction being processed by a Web service is suffering adversely from interference. The results can then be used by a management controller to trigger remedial actions, e.g., reporting problems to the system manager or switching cloud providers. Results using a realistic Web benchmark show that the approach is effective. The most effective variant of our approach is able to detect about 96% of performance interference events with almost no false alarms.